Reconciling model-X and doubly robust approaches to conditional independence testing
Reconciling model-X and doubly robust approaches to conditional independence testing Page
1 The Annals of Statistics 2024, Vol. 52, No. 3, 895–921 https://doi.org/10.1214/24-AOS2372 © …
1 The Annals of Statistics 2024, Vol. 52, No. 3, 895–921 https://doi.org/10.1214/24-AOS2372 © …
Differentially Private Permutation Tests: Applications to Kernel Methods
Recent years have witnessed growing concerns about the privacy of sensitive data. In
response to these concerns, differential privacy has emerged as a rigorous framework for …
response to these concerns, differential privacy has emerged as a rigorous framework for …
Dimension-agnostic inference using cross U-statistics
Additional results are provided in the supplementary material [43]. Appendix A discusses
multiple sample-splitting, while Appendix B describes a general strategy for studying the …
multiple sample-splitting, while Appendix B describes a general strategy for studying the …
The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
Kernel techniques are among the most influential approaches in data science and statistics.
Under mild conditions, the reproducing kernel Hilbert space associated to a kernel is …
Under mild conditions, the reproducing kernel Hilbert space associated to a kernel is …
Dimension-agnostic inference using cross U-statistics
Classical asymptotic theory for statistical inference usually involves calibrating a statistic by
fixing the dimension $ d $ while letting the sample size $ n $ increase to infinity. Recently …
fixing the dimension $ d $ while letting the sample size $ n $ increase to infinity. Recently …
Robust Kernel Hypothesis Testing under Data Corruption
We propose two general methods for constructing robust permutation tests under data
corruption. The proposed tests effectively control the non-asymptotic type I error under data …
corruption. The proposed tests effectively control the non-asymptotic type I error under data …
Half-KFN: An Enhanced Detection Method for Subtle Covariate Drift
B Wang, D Xu, Y Tang - arXiv preprint arXiv:2410.08782, 2024 - arxiv.org
Detecting covariate drift is a common task of significant practical value in supervised
learning. Once covariate drift occurs, the models may no longer be applicable, hence …
learning. Once covariate drift occurs, the models may no longer be applicable, hence …
Studentized Tests of Independence: Random-Lifter approach
Z Gao, R Wang, X Wang, H Zhang - arXiv preprint arXiv:2410.18437, 2024 - arxiv.org
The exploration of associations between random objects with complex geometric structures
has catalyzed the development of various novel statistical tests encompassing distance …
has catalyzed the development of various novel statistical tests encompassing distance …
Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing
We propose a novel method to efficiently learn significant Fourier feature pairs for
maximizing the power of Hilbert-Schmidt Independence Criterion~(HSIC) based …
maximizing the power of Hilbert-Schmidt Independence Criterion~(HSIC) based …